deberta-v3-base-figurative
This model is a fine-tuned version of microsoft/deberta-v3-base on an unknown dataset. It achieves the following results on the evaluation set:
- Loss: 0.3840
- Macro F1: 0.8727
- Literal F1: 0.8941
- Idiom F1: 0.9697
- Metaphor F1: 0.7290
- Simile F1: 0.8980
Model description
More information needed
Intended uses & limitations
More information needed
Training and evaluation data
More information needed
Training procedure
Training hyperparameters
The following hyperparameters were used during training:
- learning_rate: 2e-05
- train_batch_size: 16
- eval_batch_size: 16
- seed: 42
- gradient_accumulation_steps: 2
- total_train_batch_size: 32
- optimizer: Use OptimizerNames.ADAMW_TORCH_FUSED with betas=(0.9,0.999) and epsilon=1e-08 and optimizer_args=No additional optimizer arguments
- lr_scheduler_type: linear
- lr_scheduler_warmup_steps: 0.06
- num_epochs: 20
Training results
| Training Loss | Epoch | Step | Validation Loss | Macro F1 | Literal F1 | Idiom F1 | Metaphor F1 | Simile F1 |
|---|---|---|---|---|---|---|---|---|
| 0.7461 | 1.0 | 1425 | 0.3552 | 0.8241 | 0.8282 | 0.9568 | 0.6762 | 0.8352 |
| 0.6193 | 2.0 | 2850 | 0.2795 | 0.8446 | 0.8576 | 0.9637 | 0.6910 | 0.8659 |
| 0.4072 | 3.0 | 4275 | 0.3218 | 0.8726 | 0.8987 | 0.9710 | 0.7352 | 0.8855 |
| 0.3003 | 4.0 | 5700 | 0.3840 | 0.8727 | 0.8941 | 0.9697 | 0.7290 | 0.8980 |
| 0.2481 | 5.0 | 7125 | 0.4906 | 0.8679 | 0.8978 | 0.9711 | 0.7191 | 0.8835 |
| 0.1805 | 6.0 | 8550 | 0.4938 | 0.8700 | 0.9017 | 0.9745 | 0.7207 | 0.8830 |
Framework versions
- Transformers 5.12.1
- Pytorch 2.12.1+cu130
- Datasets 5.0.0
- Tokenizers 0.22.2